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Gold-standard for computer-assisted morphological sperm analysis.

Violeta Chang¹, Alejandra Garcia², Nancy Hitschfeld³

¹Department of Computer Science, University of Chile, Beauchef 851, 3rd Floor, Santiago, RM, Chile; Laboratory for Scientific Image Analysis, SCIAN-Lab, Centro de Espermiograma Digital Asistido por Internet (CEDAI SpA), Centro de Informatica Medica y Telemedicina (CIMT), Centro Nacional en Sistemas de Informacion en Salud (CENS), Biomedical Neuroscience Institute (BNI), Instituo de Ciencias Biomedicas (ICBM), Faculty of Medicine, University of Chile, Av. Independencia 1027, Independencia, RM, Chile.

Computers in Biology and Medicine

|March 11, 2017

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A new gold-standard dataset for human sperm head classification (SCIAN-MorphoSpermGS) was created due to limitations in existing public data. Current classification methods show high variability and poor accuracy, highlighting the need for specialized approaches.

Area of Science:

Reproductive biology
Medical image analysis
Computational pathology

Background:

Existing algorithms for human sperm head classification lack standardized, publicly available datasets for comparison.
This limits the evaluation and advancement of competing methods in semen analysis.

Purpose of the Study:

Introduce SCIAN-MorphoSpermGS, a gold-standard dataset for morphological sperm analysis.
Establish a baseline for evaluating and comparing sperm head classification techniques.

Main Methods:

Developed a gold-standard dataset (SCIAN-MorphoSpermGS) with expert-classified human sperm head images.
Compared four supervised learning methods and three shape-based descriptors using this dataset.
Utilized Fleiss' Kappa Coefficient and Fisher's exact test for statistical analysis.

Keywords:

Gold-standard Infertility Morphological sperm analysis Sperm classification base-line Sperm head classification

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Main Results:

Confirmed significant inter-expert variability in morphological sperm analysis.
No single standard descriptor or classification approach proved optimal for sperm head classification.
The best classification rate achieved was 49% using Fourier descriptors and Support Vector Machines (SVM).

Conclusions:

SCIAN-MorphoSpermGS serves as a crucial tool for evaluating sperm head characterization and classification methods.
There is a clear need for novel, sperm-specific descriptors and classification approaches to address high variability in abnormal sperm cells.